Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-18T16:12:04.407Z Has data issue: false hasContentIssue false

Alternating direction method of multiplier for solving electromagnetic inverse scattering problems

Published online by Cambridge University Press:  11 March 2020

Jian Liu
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Huilin Zhou*
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Liangbing Chen
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Tao Ouyang
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
*
Author for correspondence: Huilin Zhou, E-mail: [email protected]

Abstract

In this paper, a novel alternating direction method of multiplier (ADMM) is proposed to solve the inverse scattering problems. The proposed method is suitable for a wide range of applications with electromagnetic detection. In order to solve the internal ill-posed problem of the integral equation and make the reconstructed images more closer to the ground truth, first, the augmented Lagrangian method is introduced to transform the complex constrained optimization problem into the extremum problem of unconstrained cost function. Therefore, two artificial regularization factors of the cost function are optimized. Then, this proposed method decomposes the unconstrained global problem in the inversion process into three linear sub-problem forms of contrast source function, contrast function, and dual variables. And the form of the updated algebra for each sub-problem is not complicated. By cross-iterating and updating contrast source function, contrast function, and dual variables, the global minimization of the cost function can be accurately found. Finally, the proposed method is compared with the existing well-known iterative method for solving the inverse scattering problem. Both the numerical and experimental tests verify the validity and accuracy of the proposed ADMM.

Type
Radar
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Song, X, Li, M, Yang, F, Xu, S and Abubakar, A (2019) Study on joint inversion algorithm of acoustic and electromagnetic data in biomedical imaging. IEEE Journal on Multiscale and Multiphysics Computational Techniques, 4, 211.CrossRefGoogle Scholar
2.Palmeri, R, Bevacqua, MT, Crocco, L, Isernia, T and Donato, LD (2017) Microwave imaging via distorted iterated virtual experiments. IEEE Transactions on Antennas and Propagation, 65, 829838.CrossRefGoogle Scholar
3.Chen, G, Stang, J, Haynes, M, Leuthardt, E and Moghaddam, M (2018) Real-time three-dimensional microwave monitoring of interstitial thermal therapy. IEEE Transactions on Biomedical Engineering, 65, 528538.CrossRefGoogle ScholarPubMed
4.Ireland, D, Bialkowski, K and Abbosh, A (2013) Microwave imaging for brain stroke detection using Born iterative method. IET Microwaves, Antennas & Propagation, 7, 909915.CrossRefGoogle Scholar
5.Zhuge, X and Yarovoy, AG (2011) A sparse aperture MIMO-SAR-based UWB imaging system for concealed weapon detection. IEEE Transactions on Geoscience and Remote Sensing, 49, 509518.CrossRefGoogle Scholar
6.Poli, L, Oliveri, G and Massa, A (2012) Microwave imaging within the first-order Born approximation by means of the contrast-field Bayesian compressive sensing. IEEE Transactions on Antennas and Propagation, 60, 28652879.CrossRefGoogle Scholar
7.Barriĺĺre, P, Idier, J, Goussard, Y and Laurin, J (2010) Fast solutions of the 2D inverse scattering problem based on a TSVD approximation of the internal field for the forward model. IEEE Transactions on Antennas and Propagation, 58, 40154024.CrossRefGoogle Scholar
8.van den Berg, PM and Kleinman, RE (1997) A contrast source inversion method. Inverse Problems, 13, 16071620.CrossRefGoogle Scholar
9.Chen, X (2010) Subspace-based optimization method for solving inverse-scattering problems. IEEE Transactions on Geoscience and Remote Sensing, 48, 4249.CrossRefGoogle Scholar
10.Liang, B, Qiu, C, Han, F, Zhu, C, Liu, N, Liu, H, Liu, F, Fang, G and Liu, Q (2018) A new inversion method based on distorted Born iterative method for grounded electrical source airborne transient electromagnetics. IEEE Transactions on Geoscience and Remote Sensing, 56, 877887.CrossRefGoogle Scholar
11.Sung, Y and Dasari, RR (2011) Deterministic regularization of three-dimensional optical diffraction tomography. Journal of the Optical Society of America, 28, 15541561.CrossRefGoogle ScholarPubMed
12.Kim, T, Zhou, R, Mir, M, Babacan, SD, Carney, PS, Goddard, LL and Popescu, G (2014) White-light diffraction tomography of unlabelled live cells. Nature Photonics, 8, 256263.CrossRefGoogle Scholar
13.Abdullah, H and Louis, AK (1999) The approximate inverse for solving an inverse scattering problem for acoustic waves in an inhomogeneous medium. Inverse Problems, 15, 12131229.CrossRefGoogle Scholar
14.Li, L, Wang, LG, Ding, J, Liu, PK, Xia, MY and Cui, TJ (2017) A probabilistic model for the nonlinear electromagnetic inverse scattering: TM case. IEEE Transactions on Antennas and Propagation, 65, 59845991.CrossRefGoogle Scholar
15.Liu, Z (2019) A new scheme based on Born iterative method for solving inverse scattering problems with noise disturbance. IEEE Geoscience and Remote Sensing Letters, 16, 10211025.CrossRefGoogle Scholar
16.Liu, Z and Nie, Z (2019) Subspace-based variational Born iterative method for solving inverse scattering problems. IEEE Geoscience and Remote Sensing Letters, 16, 10171020.CrossRefGoogle Scholar
17.Su, H, Xu, F, Lu, S and Jin, Y (2016) Iterative ADMM for inverse FE-BI problem: a potential solution to radio tomography of asteroids. IEEE Geoscience and Remote Sensing Letters, 54, 52265238.CrossRefGoogle Scholar
18.Abubakar, A and van den Berg, PM (2002) The contrast source inversion method for location and shape reconstructions. Inverse Problems, 18, 495510.CrossRefGoogle Scholar
19.Ji, S and Ye, J (2009) An accelerated gradient method for trace norm minimization. The 26th Annual International Conference on Machine Learning, Canada, June 2009. Montreal, Canada: International Machine Learning Society (IMLS).CrossRefGoogle Scholar
20.Deng, W and Yin, W (2016) On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing, 66, 889916.CrossRefGoogle Scholar
21.Wang, X, Aboutanios, E and Amin, MG (2014) Thinned array beampattern synthesis by iterative soft-thresholding-based optimization algorithms. IEEE Transactions on Antennas and Propagation, 62, 61026113.CrossRefGoogle Scholar
22.Cohen, G, Afshar, S, Tapson, J and van Schaik, A (2017) EMNIST: An extension of MNIST to handwritten letters. Arxiv:1702.05373.Google Scholar
23.Geffrin, JM, Sabouroux, P and Eyraud, C (2005) Free space experimental scattering database continuation: experimental set-up and measurement precision. Inverse Problems, 21, S117S130.CrossRefGoogle Scholar